Before and during COVID-19: A Cohesion Network Analysis of students' online participation in moodle courses. (August 2021)

• Record Type: Journal Article
• Authors: Dascalu, Maria-Dorinela; Ruseti, Stefan; Dascalu, Mihai; McNamara, Danielle S.; Carabas, Mihai; Rebedea, Traian; Trausan-Matu, Stefan
• Is Part Of: Computers in human behavior. Volume 121(2021)
• Publication Date: 2021-08
• Subjects: Cohesion Network Analysis -- Moodle -- Click-stream data -- Sociograms -- Learning patterns -- Student behavior -- Learner interactions; Interactive computer systems -- Periodicals; Man-machine systems -- Periodicals; 004.019
• DOI: 10.1016/j.chb.2021.106780
• Languages: English
• ISSNs: 0747-5632

Abstract

Abstract: The COVID-19 pandemic has changed the entire world, while the impact and usage of online learning environments has greatly increased. This paper presents a new version of the ReaderBench framework, grounded in Cohesion Network Analysis, which can be used to evaluate the online activity of students as a plug-in feature to Moodle. A Recurrent Neural Network with LSTM cells that combines global features, including participation and initiation indices, with a time series analysis on timeframes is used to predict student grades, while multiple sociograms are generated to observe interaction patterns. Students' behaviors and interactions are compared before and during COVID-19 using two consecutive yearly instances of an undergraduate course in Algorithm Design, conducted in Romanian using Moodle. The COVID-19 outbreak generated an off-balance, a drastic increase in participation, followed by a decrease towards the end of the semester, compared to the academic year 2018–2019 when lower fluctuations in participation were observed. The prediction model for the 2018–2019 academic year obtained an R 2 of 0.27, while the model for the second year obtained a better R 2 of 0.34, a value arguably attributable to an increased volume of online activity. Moreover, the best model from the first academic year is partially generalizable to the second year, but explains a considerably lower variance ( R 2 = 0.13). In addition to the quantitative analysis, a qualitative analysis of changes in student behaviors using comparative sociograms further supported conclusions that there were drastic changes in student behaviors observed as a function of the COVID-19 pandemic. Highlights: We evaluate students' behaviors before and during COVID-19 as a plug-in to Moodle. A RNN combines global features with time series analysis to predict student grades. Multiple sociograms are generated to observe interaction patterns. A significant increase in online participation is observed during the pandemic. The prediction model obtained an R 2 of 0.27 and 0.34 for the two academic years.